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Record W2995491708 · doi:10.1109/iecon.2019.8926966

Sinusoidal PWM for Flying Capacitor Voltage Balancing of a Six-Level Inverter

2019· article· en· W2995491708 on OpenAlexaff
Ahoora Bahrami, Guo Chen, Mehdi Narimani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInverterPulse-width modulationCapacitorVoltageMATLABSpace vector modulationComputer scienceControl theory (sociology)Electronic engineeringModulation (music)Topology (electrical circuits)EngineeringElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper presents a control technique developed based on Sinusoidal Pulse Width Modulation (SPWM) to control the flying capacitors voltages' of a recently published six-level topology inverter. The presented inverter has a fewer number of components compared to the existing six-level topologies, which makes it a cost-efficient solution for medium-voltage applications. To produce a six-level voltage at the output of the inverter, the flying capacitors voltages should be controlled at their nominal values. The six-level inverter is controlled based on Space Vector Modulation scheme (SVM), which is complex and not convenient to implement. Therefore, a control method based on Sinusoidal Pulse Width Modulation (SPWM) technique is developed to control the capacitors voltages at different operating conditions. The simulation results in MATLAB/Simulink environment has been obtained to show the performance of the developed control method. A prototype of the proposed inverter is implemented, and the experimental results are given to show the feasibility of the proposed inverter with the developed control technique.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.205
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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